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Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020 Abstract The peak of Internet searches and social media data about the coronavirus disease 2019 (COVID-19) outbreak occurred 10–14 days earlier than the peak of daily incidences in China. Internet searches and social media data had high correlation with daily incidences, with the maximum r > 0.89 in all correlations. The lag correlations also showed a maximum correlation at 8–12 days for laboratory-confirmed cases and 6–8 days for suspected cases. The coronavirus disease 2019 (COVID-19) outbreak began in Wuhan, China, in late December 2019 and quickly spread to other cities in China in a matter of days [1,2]. It was announced as a public health emergency of international concern by the World Health Organization (WHO) on 30 January 2020 [3]. Predicting the development of the outbreak as early and as reliably as possible is critical for action to prevent its spread. Internet searches and social media data have been reported to correlate with traditional surveillance data and can even predict the outbreak of disease epidemics several days or weeks earlier [4-9]. In this study, we aimed to evaluate the prediction value of the Internet search data from web-based search engines and social media for the COVID-19 outbreak in China. Trends in daily laboratory-confirmed and suspected COVID-19 cases and Internet data The daily numbers of new laboratory-confirmed cases and suspected cases of COVID-19 were collected from the data published by the National Health Commission of China (NHC, http://www.nhc.gov.cn/). A laboratory-confirmed case of COVID-19 was defined a patient with positive real-time RT-PCR to SARS-CoV-2, while a suspected case was defined as a patient with history of travelling to Wuhan City or in contact with COVID-19 cases in the 14 days before onset of symptoms and with clinical manifestation of fever, respiratory illness, pneumonia on computed tomography (CT) scan, and/or reduced white blood cells count, but no RT-PCR results. The study period was set between 16 January and 11 February 2020, because the diagnosis criteria were set on 16 January 2020. The results showed that the peak of daily new laboratory-confirmed cases was 3,887 on 4 February and the peak of daily new suspected cases was 5,328 on 5 February 2020. Daily trend data related to specific search terms were acquired from Google Trends, Baidu Index, and Sina Weibo Index by setting the time parameter to ‘2 January to 12 February 2020’ and the location parameter to ‘China’. We chose a period 2 weeks earlier than for the molecular diagnosis data for COVID-2019. Two keywords, ‘coronavirus’ and ‘pneumonia’, were used in Google Trends. The respective Chinese terms, ‘冠状病毒‘ and ‘肺炎’ were used in Baidu Index, the most popular web search engine in China, and Sina Weibo Index, a social media platform widely used in China. The peak number of search queries in Baidu was 682,888 for ‘coronavirus’ and 760,460 for ‘pneumonia’, both on 25 January 2020. The peak number of posts on Sina Weibo was 26,297,746 for ‘coronavirus’ and 30,704,753 for ‘pneumonia’, both on 21 January 2020. Google Trends does not provide the raw number of search queries but the number normalised to the peak number. The peaks for both keywords on Google Trends were reached on 25 January 2020. Figure 1 shows the overall trends of data from the keyword search for ‘coronavirus’ (or ‘冠状病毒’) and ‘pneumonia’ (or ‘肺炎’) via Google Trends, Baidu Index and Sina Weibo Index, and the number of daily new laboratory-confirmed and suspected COVID-19 cases. The data from Baidu Index, Sina Weibo Index and national COVID-19 daily incidence data were also normalised to the peak number, so that the values fall into the same range (0–100) during that period. Figure 1 Searches for keywords ‘coronavirus’ and ‘pneumonia’, obtained via different indices, and number of daily new COVID-19 cases, China, January–February 2020 Lag correlation between daily laboratory-confirmed/suspected cases and Internet searches Figure 2 and the Table showed the lag Spearman correlations between the daily new laboratory-confirmed cases (upper panel) and suspected cases (lower panel) of COVID-19 and the Internet search data from Google Trends, Baidu Index and Sina Weibo Index. We found a high correlation with the Internet search data (r > 0.7) 8–10 days earlier for new laboratory-confirmed cases, and 5-7 days earlier for new suspected cases. Figure 2 Lag correlations between new laboratory-confirmed cases and suspected cases of COVID-19 and data from Google Trends, Baidu Index and Weibo Index for the keywords ‘coronavirus’ and ‘pneumonia’, China, January–February 2020 Table Lag correlation coefficients and p values between Internet search data and daily new laboratory-confirmed/suspected COVID-19 cases, China, January–February 2020 Days earlier Google Trends Baidu Index Sina Weibo Index Coronavirus p Pneumonia p Coronavirus p Pneumonia p Coronavirus p Pneumonia p New laboratory-confirmed cases 0 0.176 0.370 −0.035 0.861 0.021 0.917 0.129 0.513 0.106 0.593 0.109 0.582 1 0.324 0.093 0.122 0.537 0.160 0.416 0.265 0.172 0.202 0.303 0.190 0.332 2 0.455 0.015 0.271 0.164 0.299 0.122 0.411 0.030 0.346 0.072 0.298 0.123 3 0.561 0.002 0.388 0.041 0.406 0.032 0.516 0.005 0.431 0.022 0.408 0.031 4 0.672 < 0.001 0.505 0.006 0.529 0.004 0.641 < 0.001 0.498 0.007 0.470 0.012 5 0.779 < 0.001 0.606 0.001 0.624 < 0.001 0.722 < 0.001 0.562 0.002 0.553 0.002 6 0.850 < 0.001 0.712 < 0.001 0.706 < 0.001 0.808 < 0.001 0.679 < 0.001 0.668 < 0.001 7 0.902 < 0.001 0.777 < 0.001 0.750 < 0.001 0.861 < 0.001 0.751 < 0.001 0.754 < 0.001 8 0.944 < 0.001 0.835 < 0.001 0.823 < 0.001 0.902 < 0.001 0.829 < 0.001 0.817 < 0.001 9 0.958 < 0.001 0.878 < 0.001 0.887 < 0.001 0.892 < 0.001 0.876 < 0.001 0.872 < 0.001 10 0.953 < 0.001 0.893 < 0.001 0.928 < 0.001 0.873 < 0.001 0.921 < 0.001 0.899 < 0.001 11 0.924 < 0.001 0.845 < 0.001 0.925 < 0.001 0.786 < 0.001 0.917 < 0.001 0.875 < 0.001 12 0.857 < 0.001 0.818 < 0.001 0.933 < 0.001 0.715 < 0.001 0.944 < 0.001 0.875 < 0.001 13 0.815 < 0.001 0.762 < 0.001 0.908 < 0.001 0.609 0.001 0.916 < 0.001 0.812 < 0.001 14 0.783 < 0.001 0.697 < 0.001 0.858 < 0.001 0.496 0.007 0.885 < 0.001 0.733 < 0.001 New suspected cases 0 −0.003 0.989 −0.372 0.073 −0.309 0.142 −0.091 0.671 −0.279 0.187 −0.309 0.142 1 0.246 0.246 −0.116 0.590 −0.068 0.753 0.141 0.511 −0.078 0.716 −0.103 0.630 2 0.413 0.045 0.104 0.630 0.125 0.560 0.346 0.098 0.089 0.680 0.050 0.818 3 0.614 0.001 0.312 0.138 0.352 0.091 0.551 0.005 0.253 0.233 0.248 0.243 4 0.768 < 0.001 0.514 0.010 0.538 0.007 0.697 < 0.001 0.431 0.035 0.383 0.065 5 0.832 < 0.001 0.687 < 0.001 0.670 < 0.001 0.816 < 0.001 0.520 0.009 0.501 0.013 6 0.912 < 0.001 0.771 < 0.001 0.725 < 0.001 0.895 < 0.001 0.672 < 0.001 0.670 < 0.001 7 0.933 < 0.001 0.850 < 0.001 0.830 < 0.001 0.914 < 0.001 0.813 < 0.001 0.872 < 0.001 8 0.875 < 0.001 0.960 < 0.001 0.906 < 0.001 0.926 < 0.001 0.924 < 0.001 0.907 < 0.001 9 0.787 < 0.001 0.865 < 0.001 0.882 < 0.001 0.850 < 0.001 0.883 < 0.001 0.899 < 0.001 10 0.744 < 0.001 0.827 < 0.001 0.841 < 0.001 0.766 < 0.001 0.818 < 0.001 0.832 < 0.001 11 0.671 < 0.001 0.770 < 0.001 0.790 < 0.001 0.698 < 0.001 0.781 < 0.001 0.802 < 0.001 12 0.544 0.006 0.693 < 0.001 0.686 < 0.001 0.559 0.005 0.697 < 0.001 0.683 < 0.001 13 0.482 0.017 0.578 0.003 0.583 0.003 0.454 0.026 0.622 0.001 0.600 0.002 14 0.497 0.013 0.448 0.028 0.547 0.006 0.288 0.173 0.609 0.002 0.550 0.005 Shaded text: high correlation with r > 0.7. Text in italics: highest correlation. For new laboratory-confirmed cases, the highest correlation was found 9, 12 and 12 days earlier for searches for the keyword ‘coronavirus’ in Google Trends, Baidu Index and Sina Weibo Index with, respectively, r = 0.958, 0.933 and 0.944. For the keyword ‘pneumonia’, the highest correlation was found 10, 8 and 10 days earlier in Google Trends, Baidu Index and Sina Weibo Index, with r = 0.893, 0.944 and 0.899, respectively. The lag correlation of new suspected cases was similar to the laboratory-confirmed cases, with a shorter lag time. The highest correlation was found 6, 8 and 8 days earlier for searches for the keyword ‘coronavirus’ in Google Trends, Baidu Index and Sina Weibo Index, with r = 0.912, 0.906 and 0.924, respectively. For the keyword ‘pneumonia’, the highest correlation was found all 8 days earlier in Google Trends, Baidu Index and Sina Weibo Index, with r = 0.960, 0.926 and 0.907, respectively. Discussion Our study demonstrated that the data obtained from Google Trends, Baidu Index and Sina Weibo Index on searches for the keywords ‘coronavirus’ and ‘pneumonia’ correlated with the published NHC data on daily incidence of laboratory-confirmed and suspected cases of COVID-19, with the maximum r > 0.89. We also found that the peak interest for these keywords in Internet search engines and social media data was 10–14 days earlier than the incidence peak of COVID-19 published by the NHC. The lag correlation showed a maximum correlation at 8–12 days for laboratory-confirmed cases and 6–8 days for suspected cases. COVID-19 is a rapidly spreading infectious disease with, at the time of submission, more than 80,000 cases and a mortality so far known to be 3.4% [10]. It is important to predict the development of this outbreak as early and as reliably as possible, in order to take action to prevent its spread. Our data showed that the two popularly used Internet search engines, Google and Baidu, and the social media platform, Sina Weibo, were able to predict the disease outbreak 1–2 weeks earlier than the traditional surveillance systems. The role of Internet surveillance tools in early prediction of other epidemics has been reported previously, including for influenza [4], dengue fever [5], H1N1 [6], Zika [7], measles [8] and Middle East respiratory syndrome [9]. The availability of early information about infectious diseases through Internet search engines and social media will be helpful for making decisions related to disease control and prevention. Internet search data have been shown to enable the monitoring of Middle East respiratory syndrome 3 days before laboratory confirmations [9]. However, our results showed a much longer lag time for reported new laboratory-confirmed and suspected COVID-19 cases compared with digital surveillance data. There are several explanations. Firstly, COVID-19 is a novel disease just recently recognised. The first version of a guideline for diagnosis and management of COVID-19 was announced on 16 January 2020. It took time for the medical professionals to learn about the virus and the disease in order to make correct diagnosis. Secondly, the diagnosis of COVID-19 requires two independent confirmatory laboratory tests, which should be taken at least 1 day apart. Our results showed that the lag correlation is shorter for the suspected than for laboratory-confirmed cases. Thirdly, the supply of laboratory testing kits may have been insufficient in the early stages of the coronavirus outbreak, which would have limited the number of patients that can be confirmed. Finally, the Internet searches and social media mentions are not only initiated by the patients and their family members, but also globally by the general public who are concerned about this rapidly spreading disease. In addition, we found that the data from the Baidu Index and Sina Weibo Index could monitor the number of daily new confirmed and suspected cases from the NHC earlier than the data from Google Trends. A possible explanation is that the Google is not a major search engine used in China, where Baidu and Sina Weibo are widely used. The peak in the Sina Weibo Index was reached earlier than in Google Trends and Baidu Index. This suggests that Sina Weibo, which also serves as a social medium, disseminated the information faster than traditional websites. COVID-19 was firstly reported as ‘pneumonia of unknown aetiology’ or ‘pneumonia of unknown cause’ in late December 2019. On 8 January 2020, a novel coronavirus was identified as the cause of this disease. The disease was first named Novel coronavirus pneumonia by the NHC of China on 8 February and later ‘coronavirus disease 2019’ (abbreviated ‘COVID-19’) on 11 February by the WHO. Our search period was defined from January 16 to February 11. Therefore, we think that the two keywords ‘pneumonia’ and ‘coronavirus’ were sufficient to include most Internet content related to COVID-19 in this period. We also used other terms such as ‘新冠‘ (novel coronavirus), ‘新型冠状病毒肺炎’ (novel coronavirus pneumonia) as keywords but they returned much smaller numbers of queries and posts and we did therefore not include them in the analysis. It is also notable that the strength of correlation was different for different keywords. On Google, the keyword ‘coronavirus’ had the highest correlation coefficient (r = 0.958) with daily new laboratory-confirmed cases, and ‘pneumonia’ had the highest correlation coefficient with daily new suspected cases (r = 0.960). We found the same pattern in the Baidu Index and Sina Weibo Index. An explanation could be that ‘coronavirus’ is linked to the viral pathogen which should be investigated by a laboratory test, while ‘pneumonia’ is a clinical term and should link stronger to the suspected cases that are based on clinical and imaging evidence. A limitation of our study is its retrospective nature. If the Internet search engines and social media data were used in a real-time surveillance system, finding the best lag time would be a challenge because we would not have any training data to calibrate the analysis for a new disease. Conclusion This study reveals the advantages of Internet surveillance using Sina Weibo Index, Google Trends and Baidu Index to monitor a new infectious disease. Reliable data can be obtained early at low cost. The Internet surveillance data provided an accurate and timely prediction about the outbreak and progression of COVID-19. Acknowledgements Funding statement: This study was supported by the Grant for Key Disciplinary Project of Clinical Medicine under the Guangdong High-level University Development Program (002-18119101). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Conflict of interest: None declared. Authors’ contributions: CL and LJC collected the data, analysed the data and drafted the manuscript. XC, LJC, CPP and HC revised the manuscript. MZ and HC convened the idea and designed the study.

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article-title	Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020
abstract	The peak of Internet searches and social media data about the coronavirus disease 2019 (COVID-19) outbreak occurred 10–14 days earlier than the peak of daily incidences in China. Internet searches and social media data had high correlation with daily incidences, with the maximum r > 0.89 in all correlations. The lag correlations also showed a maximum correlation at 8–12 days for laboratory-confirmed cases and 6–8 days for suspected cases.
p	The peak of Internet searches and social media data about the coronavirus disease 2019 (COVID-19) outbreak occurred 10–14 days earlier than the peak of daily incidences in China. Internet searches and social media data had high correlation with daily incidences, with the maximum r > 0.89 in all correlations. The lag correlations also showed a maximum correlation at 8–12 days for laboratory-confirmed cases and 6–8 days for suspected cases.
body	The coronavirus disease 2019 (COVID-19) outbreak began in Wuhan, China, in late December 2019 and quickly spread to other cities in China in a matter of days [1,2]. It was announced as a public health emergency of international concern by the World Health Organization (WHO) on 30 January 2020 [3]. Predicting the development of the outbreak as early and as reliably as possible is critical for action to prevent its spread. Internet searches and social media data have been reported to correlate with traditional surveillance data and can even predict the outbreak of disease epidemics several days or weeks earlier [4-9]. In this study, we aimed to evaluate the prediction value of the Internet search data from web-based search engines and social media for the COVID-19 outbreak in China. Trends in daily laboratory-confirmed and suspected COVID-19 cases and Internet data The daily numbers of new laboratory-confirmed cases and suspected cases of COVID-19 were collected from the data published by the National Health Commission of China (NHC, http://www.nhc.gov.cn/). A laboratory-confirmed case of COVID-19 was defined a patient with positive real-time RT-PCR to SARS-CoV-2, while a suspected case was defined as a patient with history of travelling to Wuhan City or in contact with COVID-19 cases in the 14 days before onset of symptoms and with clinical manifestation of fever, respiratory illness, pneumonia on computed tomography (CT) scan, and/or reduced white blood cells count, but no RT-PCR results. The study period was set between 16 January and 11 February 2020, because the diagnosis criteria were set on 16 January 2020. The results showed that the peak of daily new laboratory-confirmed cases was 3,887 on 4 February and the peak of daily new suspected cases was 5,328 on 5 February 2020. Daily trend data related to specific search terms were acquired from Google Trends, Baidu Index, and Sina Weibo Index by setting the time parameter to ‘2 January to 12 February 2020’ and the location parameter to ‘China’. We chose a period 2 weeks earlier than for the molecular diagnosis data for COVID-2019. Two keywords, ‘coronavirus’ and ‘pneumonia’, were used in Google Trends. The respective Chinese terms, ‘冠状病毒‘ and ‘肺炎’ were used in Baidu Index, the most popular web search engine in China, and Sina Weibo Index, a social media platform widely used in China. The peak number of search queries in Baidu was 682,888 for ‘coronavirus’ and 760,460 for ‘pneumonia’, both on 25 January 2020. The peak number of posts on Sina Weibo was 26,297,746 for ‘coronavirus’ and 30,704,753 for ‘pneumonia’, both on 21 January 2020. Google Trends does not provide the raw number of search queries but the number normalised to the peak number. The peaks for both keywords on Google Trends were reached on 25 January 2020. Figure 1 shows the overall trends of data from the keyword search for ‘coronavirus’ (or ‘冠状病毒’) and ‘pneumonia’ (or ‘肺炎’) via Google Trends, Baidu Index and Sina Weibo Index, and the number of daily new laboratory-confirmed and suspected COVID-19 cases. The data from Baidu Index, Sina Weibo Index and national COVID-19 daily incidence data were also normalised to the peak number, so that the values fall into the same range (0–100) during that period. Figure 1 Searches for keywords ‘coronavirus’ and ‘pneumonia’, obtained via different indices, and number of daily new COVID-19 cases, China, January–February 2020 Lag correlation between daily laboratory-confirmed/suspected cases and Internet searches Figure 2 and the Table showed the lag Spearman correlations between the daily new laboratory-confirmed cases (upper panel) and suspected cases (lower panel) of COVID-19 and the Internet search data from Google Trends, Baidu Index and Sina Weibo Index. We found a high correlation with the Internet search data (r > 0.7) 8–10 days earlier for new laboratory-confirmed cases, and 5-7 days earlier for new suspected cases. Figure 2 Lag correlations between new laboratory-confirmed cases and suspected cases of COVID-19 and data from Google Trends, Baidu Index and Weibo Index for the keywords ‘coronavirus’ and ‘pneumonia’, China, January–February 2020 Table Lag correlation coefficients and p values between Internet search data and daily new laboratory-confirmed/suspected COVID-19 cases, China, January–February 2020 Days earlier Google Trends Baidu Index Sina Weibo Index Coronavirus p Pneumonia p Coronavirus p Pneumonia p Coronavirus p Pneumonia p New laboratory-confirmed cases 0 0.176 0.370 −0.035 0.861 0.021 0.917 0.129 0.513 0.106 0.593 0.109 0.582 1 0.324 0.093 0.122 0.537 0.160 0.416 0.265 0.172 0.202 0.303 0.190 0.332 2 0.455 0.015 0.271 0.164 0.299 0.122 0.411 0.030 0.346 0.072 0.298 0.123 3 0.561 0.002 0.388 0.041 0.406 0.032 0.516 0.005 0.431 0.022 0.408 0.031 4 0.672 < 0.001 0.505 0.006 0.529 0.004 0.641 < 0.001 0.498 0.007 0.470 0.012 5 0.779 < 0.001 0.606 0.001 0.624 < 0.001 0.722 < 0.001 0.562 0.002 0.553 0.002 6 0.850 < 0.001 0.712 < 0.001 0.706 < 0.001 0.808 < 0.001 0.679 < 0.001 0.668 < 0.001 7 0.902 < 0.001 0.777 < 0.001 0.750 < 0.001 0.861 < 0.001 0.751 < 0.001 0.754 < 0.001 8 0.944 < 0.001 0.835 < 0.001 0.823 < 0.001 0.902 < 0.001 0.829 < 0.001 0.817 < 0.001 9 0.958 < 0.001 0.878 < 0.001 0.887 < 0.001 0.892 < 0.001 0.876 < 0.001 0.872 < 0.001 10 0.953 < 0.001 0.893 < 0.001 0.928 < 0.001 0.873 < 0.001 0.921 < 0.001 0.899 < 0.001 11 0.924 < 0.001 0.845 < 0.001 0.925 < 0.001 0.786 < 0.001 0.917 < 0.001 0.875 < 0.001 12 0.857 < 0.001 0.818 < 0.001 0.933 < 0.001 0.715 < 0.001 0.944 < 0.001 0.875 < 0.001 13 0.815 < 0.001 0.762 < 0.001 0.908 < 0.001 0.609 0.001 0.916 < 0.001 0.812 < 0.001 14 0.783 < 0.001 0.697 < 0.001 0.858 < 0.001 0.496 0.007 0.885 < 0.001 0.733 < 0.001 New suspected cases 0 −0.003 0.989 −0.372 0.073 −0.309 0.142 −0.091 0.671 −0.279 0.187 −0.309 0.142 1 0.246 0.246 −0.116 0.590 −0.068 0.753 0.141 0.511 −0.078 0.716 −0.103 0.630 2 0.413 0.045 0.104 0.630 0.125 0.560 0.346 0.098 0.089 0.680 0.050 0.818 3 0.614 0.001 0.312 0.138 0.352 0.091 0.551 0.005 0.253 0.233 0.248 0.243 4 0.768 < 0.001 0.514 0.010 0.538 0.007 0.697 < 0.001 0.431 0.035 0.383 0.065 5 0.832 < 0.001 0.687 < 0.001 0.670 < 0.001 0.816 < 0.001 0.520 0.009 0.501 0.013 6 0.912 < 0.001 0.771 < 0.001 0.725 < 0.001 0.895 < 0.001 0.672 < 0.001 0.670 < 0.001 7 0.933 < 0.001 0.850 < 0.001 0.830 < 0.001 0.914 < 0.001 0.813 < 0.001 0.872 < 0.001 8 0.875 < 0.001 0.960 < 0.001 0.906 < 0.001 0.926 < 0.001 0.924 < 0.001 0.907 < 0.001 9 0.787 < 0.001 0.865 < 0.001 0.882 < 0.001 0.850 < 0.001 0.883 < 0.001 0.899 < 0.001 10 0.744 < 0.001 0.827 < 0.001 0.841 < 0.001 0.766 < 0.001 0.818 < 0.001 0.832 < 0.001 11 0.671 < 0.001 0.770 < 0.001 0.790 < 0.001 0.698 < 0.001 0.781 < 0.001 0.802 < 0.001 12 0.544 0.006 0.693 < 0.001 0.686 < 0.001 0.559 0.005 0.697 < 0.001 0.683 < 0.001 13 0.482 0.017 0.578 0.003 0.583 0.003 0.454 0.026 0.622 0.001 0.600 0.002 14 0.497 0.013 0.448 0.028 0.547 0.006 0.288 0.173 0.609 0.002 0.550 0.005 Shaded text: high correlation with r > 0.7. Text in italics: highest correlation. For new laboratory-confirmed cases, the highest correlation was found 9, 12 and 12 days earlier for searches for the keyword ‘coronavirus’ in Google Trends, Baidu Index and Sina Weibo Index with, respectively, r = 0.958, 0.933 and 0.944. For the keyword ‘pneumonia’, the highest correlation was found 10, 8 and 10 days earlier in Google Trends, Baidu Index and Sina Weibo Index, with r = 0.893, 0.944 and 0.899, respectively. The lag correlation of new suspected cases was similar to the laboratory-confirmed cases, with a shorter lag time. The highest correlation was found 6, 8 and 8 days earlier for searches for the keyword ‘coronavirus’ in Google Trends, Baidu Index and Sina Weibo Index, with r = 0.912, 0.906 and 0.924, respectively. For the keyword ‘pneumonia’, the highest correlation was found all 8 days earlier in Google Trends, Baidu Index and Sina Weibo Index, with r = 0.960, 0.926 and 0.907, respectively. Discussion Our study demonstrated that the data obtained from Google Trends, Baidu Index and Sina Weibo Index on searches for the keywords ‘coronavirus’ and ‘pneumonia’ correlated with the published NHC data on daily incidence of laboratory-confirmed and suspected cases of COVID-19, with the maximum r > 0.89. We also found that the peak interest for these keywords in Internet search engines and social media data was 10–14 days earlier than the incidence peak of COVID-19 published by the NHC. The lag correlation showed a maximum correlation at 8–12 days for laboratory-confirmed cases and 6–8 days for suspected cases. COVID-19 is a rapidly spreading infectious disease with, at the time of submission, more than 80,000 cases and a mortality so far known to be 3.4% [10]. It is important to predict the development of this outbreak as early and as reliably as possible, in order to take action to prevent its spread. Our data showed that the two popularly used Internet search engines, Google and Baidu, and the social media platform, Sina Weibo, were able to predict the disease outbreak 1–2 weeks earlier than the traditional surveillance systems. The role of Internet surveillance tools in early prediction of other epidemics has been reported previously, including for influenza [4], dengue fever [5], H1N1 [6], Zika [7], measles [8] and Middle East respiratory syndrome [9]. The availability of early information about infectious diseases through Internet search engines and social media will be helpful for making decisions related to disease control and prevention. Internet search data have been shown to enable the monitoring of Middle East respiratory syndrome 3 days before laboratory confirmations [9]. However, our results showed a much longer lag time for reported new laboratory-confirmed and suspected COVID-19 cases compared with digital surveillance data. There are several explanations. Firstly, COVID-19 is a novel disease just recently recognised. The first version of a guideline for diagnosis and management of COVID-19 was announced on 16 January 2020. It took time for the medical professionals to learn about the virus and the disease in order to make correct diagnosis. Secondly, the diagnosis of COVID-19 requires two independent confirmatory laboratory tests, which should be taken at least 1 day apart. Our results showed that the lag correlation is shorter for the suspected than for laboratory-confirmed cases. Thirdly, the supply of laboratory testing kits may have been insufficient in the early stages of the coronavirus outbreak, which would have limited the number of patients that can be confirmed. Finally, the Internet searches and social media mentions are not only initiated by the patients and their family members, but also globally by the general public who are concerned about this rapidly spreading disease. In addition, we found that the data from the Baidu Index and Sina Weibo Index could monitor the number of daily new confirmed and suspected cases from the NHC earlier than the data from Google Trends. A possible explanation is that the Google is not a major search engine used in China, where Baidu and Sina Weibo are widely used. The peak in the Sina Weibo Index was reached earlier than in Google Trends and Baidu Index. This suggests that Sina Weibo, which also serves as a social medium, disseminated the information faster than traditional websites. COVID-19 was firstly reported as ‘pneumonia of unknown aetiology’ or ‘pneumonia of unknown cause’ in late December 2019. On 8 January 2020, a novel coronavirus was identified as the cause of this disease. The disease was first named Novel coronavirus pneumonia by the NHC of China on 8 February and later ‘coronavirus disease 2019’ (abbreviated ‘COVID-19’) on 11 February by the WHO. Our search period was defined from January 16 to February 11. Therefore, we think that the two keywords ‘pneumonia’ and ‘coronavirus’ were sufficient to include most Internet content related to COVID-19 in this period. We also used other terms such as ‘新冠‘ (novel coronavirus), ‘新型冠状病毒肺炎’ (novel coronavirus pneumonia) as keywords but they returned much smaller numbers of queries and posts and we did therefore not include them in the analysis. It is also notable that the strength of correlation was different for different keywords. On Google, the keyword ‘coronavirus’ had the highest correlation coefficient (r = 0.958) with daily new laboratory-confirmed cases, and ‘pneumonia’ had the highest correlation coefficient with daily new suspected cases (r = 0.960). We found the same pattern in the Baidu Index and Sina Weibo Index. An explanation could be that ‘coronavirus’ is linked to the viral pathogen which should be investigated by a laboratory test, while ‘pneumonia’ is a clinical term and should link stronger to the suspected cases that are based on clinical and imaging evidence. A limitation of our study is its retrospective nature. If the Internet search engines and social media data were used in a real-time surveillance system, finding the best lag time would be a challenge because we would not have any training data to calibrate the analysis for a new disease. Conclusion This study reveals the advantages of Internet surveillance using Sina Weibo Index, Google Trends and Baidu Index to monitor a new infectious disease. Reliable data can be obtained early at low cost. The Internet surveillance data provided an accurate and timely prediction about the outbreak and progression of COVID-19.
p	The coronavirus disease 2019 (COVID-19) outbreak began in Wuhan, China, in late December 2019 and quickly spread to other cities in China in a matter of days [1,2]. It was announced as a public health emergency of international concern by the World Health Organization (WHO) on 30 January 2020 [3]. Predicting the development of the outbreak as early and as reliably as possible is critical for action to prevent its spread. Internet searches and social media data have been reported to correlate with traditional surveillance data and can even predict the outbreak of disease epidemics several days or weeks earlier [4-9].
p	In this study, we aimed to evaluate the prediction value of the Internet search data from web-based search engines and social media for the COVID-19 outbreak in China.
sec	Trends in daily laboratory-confirmed and suspected COVID-19 cases and Internet data The daily numbers of new laboratory-confirmed cases and suspected cases of COVID-19 were collected from the data published by the National Health Commission of China (NHC, http://www.nhc.gov.cn/). A laboratory-confirmed case of COVID-19 was defined a patient with positive real-time RT-PCR to SARS-CoV-2, while a suspected case was defined as a patient with history of travelling to Wuhan City or in contact with COVID-19 cases in the 14 days before onset of symptoms and with clinical manifestation of fever, respiratory illness, pneumonia on computed tomography (CT) scan, and/or reduced white blood cells count, but no RT-PCR results. The study period was set between 16 January and 11 February 2020, because the diagnosis criteria were set on 16 January 2020. The results showed that the peak of daily new laboratory-confirmed cases was 3,887 on 4 February and the peak of daily new suspected cases was 5,328 on 5 February 2020. Daily trend data related to specific search terms were acquired from Google Trends, Baidu Index, and Sina Weibo Index by setting the time parameter to ‘2 January to 12 February 2020’ and the location parameter to ‘China’. We chose a period 2 weeks earlier than for the molecular diagnosis data for COVID-2019. Two keywords, ‘coronavirus’ and ‘pneumonia’, were used in Google Trends. The respective Chinese terms, ‘冠状病毒‘ and ‘肺炎’ were used in Baidu Index, the most popular web search engine in China, and Sina Weibo Index, a social media platform widely used in China. The peak number of search queries in Baidu was 682,888 for ‘coronavirus’ and 760,460 for ‘pneumonia’, both on 25 January 2020. The peak number of posts on Sina Weibo was 26,297,746 for ‘coronavirus’ and 30,704,753 for ‘pneumonia’, both on 21 January 2020. Google Trends does not provide the raw number of search queries but the number normalised to the peak number. The peaks for both keywords on Google Trends were reached on 25 January 2020. Figure 1 shows the overall trends of data from the keyword search for ‘coronavirus’ (or ‘冠状病毒’) and ‘pneumonia’ (or ‘肺炎’) via Google Trends, Baidu Index and Sina Weibo Index, and the number of daily new laboratory-confirmed and suspected COVID-19 cases. The data from Baidu Index, Sina Weibo Index and national COVID-19 daily incidence data were also normalised to the peak number, so that the values fall into the same range (0–100) during that period. Figure 1 Searches for keywords ‘coronavirus’ and ‘pneumonia’, obtained via different indices, and number of daily new COVID-19 cases, China, January–February 2020
title	Trends in daily laboratory-confirmed and suspected COVID-19 cases and Internet data
p	The daily numbers of new laboratory-confirmed cases and suspected cases of COVID-19 were collected from the data published by the National Health Commission of China (NHC, http://www.nhc.gov.cn/). A laboratory-confirmed case of COVID-19 was defined a patient with positive real-time RT-PCR to SARS-CoV-2, while a suspected case was defined as a patient with history of travelling to Wuhan City or in contact with COVID-19 cases in the 14 days before onset of symptoms and with clinical manifestation of fever, respiratory illness, pneumonia on computed tomography (CT) scan, and/or reduced white blood cells count, but no RT-PCR results. The study period was set between 16 January and 11 February 2020, because the diagnosis criteria were set on 16 January 2020. The results showed that the peak of daily new laboratory-confirmed cases was 3,887 on 4 February and the peak of daily new suspected cases was 5,328 on 5 February 2020.
p	Daily trend data related to specific search terms were acquired from Google Trends, Baidu Index, and Sina Weibo Index by setting the time parameter to ‘2 January to 12 February 2020’ and the location parameter to ‘China’. We chose a period 2 weeks earlier than for the molecular diagnosis data for COVID-2019. Two keywords, ‘coronavirus’ and ‘pneumonia’, were used in Google Trends. The respective Chinese terms, ‘冠状病毒‘ and ‘肺炎’ were used in Baidu Index, the most popular web search engine in China, and Sina Weibo Index, a social media platform widely used in China. The peak number of search queries in Baidu was 682,888 for ‘coronavirus’ and 760,460 for ‘pneumonia’, both on 25 January 2020. The peak number of posts on Sina Weibo was 26,297,746 for ‘coronavirus’ and 30,704,753 for ‘pneumonia’, both on 21 January 2020. Google Trends does not provide the raw number of search queries but the number normalised to the peak number. The peaks for both keywords on Google Trends were reached on 25 January 2020.
p	Figure 1 shows the overall trends of data from the keyword search for ‘coronavirus’ (or ‘冠状病毒’) and ‘pneumonia’ (or ‘肺炎’) via Google Trends, Baidu Index and Sina Weibo Index, and the number of daily new laboratory-confirmed and suspected COVID-19 cases. The data from Baidu Index, Sina Weibo Index and national COVID-19 daily incidence data were also normalised to the peak number, so that the values fall into the same range (0–100) during that period.
figure	Figure 1 Searches for keywords ‘coronavirus’ and ‘pneumonia’, obtained via different indices, and number of daily new COVID-19 cases, China, January–February 2020
label	Figure 1
caption	Searches for keywords ‘coronavirus’ and ‘pneumonia’, obtained via different indices, and number of daily new COVID-19 cases, China, January–February 2020
p	Searches for keywords ‘coronavirus’ and ‘pneumonia’, obtained via different indices, and number of daily new COVID-19 cases, China, January–February 2020
sec	Lag correlation between daily laboratory-confirmed/suspected cases and Internet searches Figure 2 and the Table showed the lag Spearman correlations between the daily new laboratory-confirmed cases (upper panel) and suspected cases (lower panel) of COVID-19 and the Internet search data from Google Trends, Baidu Index and Sina Weibo Index. We found a high correlation with the Internet search data (r > 0.7) 8–10 days earlier for new laboratory-confirmed cases, and 5-7 days earlier for new suspected cases. Figure 2 Lag correlations between new laboratory-confirmed cases and suspected cases of COVID-19 and data from Google Trends, Baidu Index and Weibo Index for the keywords ‘coronavirus’ and ‘pneumonia’, China, January–February 2020 Table Lag correlation coefficients and p values between Internet search data and daily new laboratory-confirmed/suspected COVID-19 cases, China, January–February 2020 Days earlier Google Trends Baidu Index Sina Weibo Index Coronavirus p Pneumonia p Coronavirus p Pneumonia p Coronavirus p Pneumonia p New laboratory-confirmed cases 0 0.176 0.370 −0.035 0.861 0.021 0.917 0.129 0.513 0.106 0.593 0.109 0.582 1 0.324 0.093 0.122 0.537 0.160 0.416 0.265 0.172 0.202 0.303 0.190 0.332 2 0.455 0.015 0.271 0.164 0.299 0.122 0.411 0.030 0.346 0.072 0.298 0.123 3 0.561 0.002 0.388 0.041 0.406 0.032 0.516 0.005 0.431 0.022 0.408 0.031 4 0.672 < 0.001 0.505 0.006 0.529 0.004 0.641 < 0.001 0.498 0.007 0.470 0.012 5 0.779 < 0.001 0.606 0.001 0.624 < 0.001 0.722 < 0.001 0.562 0.002 0.553 0.002 6 0.850 < 0.001 0.712 < 0.001 0.706 < 0.001 0.808 < 0.001 0.679 < 0.001 0.668 < 0.001 7 0.902 < 0.001 0.777 < 0.001 0.750 < 0.001 0.861 < 0.001 0.751 < 0.001 0.754 < 0.001 8 0.944 < 0.001 0.835 < 0.001 0.823 < 0.001 0.902 < 0.001 0.829 < 0.001 0.817 < 0.001 9 0.958 < 0.001 0.878 < 0.001 0.887 < 0.001 0.892 < 0.001 0.876 < 0.001 0.872 < 0.001 10 0.953 < 0.001 0.893 < 0.001 0.928 < 0.001 0.873 < 0.001 0.921 < 0.001 0.899 < 0.001 11 0.924 < 0.001 0.845 < 0.001 0.925 < 0.001 0.786 < 0.001 0.917 < 0.001 0.875 < 0.001 12 0.857 < 0.001 0.818 < 0.001 0.933 < 0.001 0.715 < 0.001 0.944 < 0.001 0.875 < 0.001 13 0.815 < 0.001 0.762 < 0.001 0.908 < 0.001 0.609 0.001 0.916 < 0.001 0.812 < 0.001 14 0.783 < 0.001 0.697 < 0.001 0.858 < 0.001 0.496 0.007 0.885 < 0.001 0.733 < 0.001 New suspected cases 0 −0.003 0.989 −0.372 0.073 −0.309 0.142 −0.091 0.671 −0.279 0.187 −0.309 0.142 1 0.246 0.246 −0.116 0.590 −0.068 0.753 0.141 0.511 −0.078 0.716 −0.103 0.630 2 0.413 0.045 0.104 0.630 0.125 0.560 0.346 0.098 0.089 0.680 0.050 0.818 3 0.614 0.001 0.312 0.138 0.352 0.091 0.551 0.005 0.253 0.233 0.248 0.243 4 0.768 < 0.001 0.514 0.010 0.538 0.007 0.697 < 0.001 0.431 0.035 0.383 0.065 5 0.832 < 0.001 0.687 < 0.001 0.670 < 0.001 0.816 < 0.001 0.520 0.009 0.501 0.013 6 0.912 < 0.001 0.771 < 0.001 0.725 < 0.001 0.895 < 0.001 0.672 < 0.001 0.670 < 0.001 7 0.933 < 0.001 0.850 < 0.001 0.830 < 0.001 0.914 < 0.001 0.813 < 0.001 0.872 < 0.001 8 0.875 < 0.001 0.960 < 0.001 0.906 < 0.001 0.926 < 0.001 0.924 < 0.001 0.907 < 0.001 9 0.787 < 0.001 0.865 < 0.001 0.882 < 0.001 0.850 < 0.001 0.883 < 0.001 0.899 < 0.001 10 0.744 < 0.001 0.827 < 0.001 0.841 < 0.001 0.766 < 0.001 0.818 < 0.001 0.832 < 0.001 11 0.671 < 0.001 0.770 < 0.001 0.790 < 0.001 0.698 < 0.001 0.781 < 0.001 0.802 < 0.001 12 0.544 0.006 0.693 < 0.001 0.686 < 0.001 0.559 0.005 0.697 < 0.001 0.683 < 0.001 13 0.482 0.017 0.578 0.003 0.583 0.003 0.454 0.026 0.622 0.001 0.600 0.002 14 0.497 0.013 0.448 0.028 0.547 0.006 0.288 0.173 0.609 0.002 0.550 0.005 Shaded text: high correlation with r > 0.7. Text in italics: highest correlation. For new laboratory-confirmed cases, the highest correlation was found 9, 12 and 12 days earlier for searches for the keyword ‘coronavirus’ in Google Trends, Baidu Index and Sina Weibo Index with, respectively, r = 0.958, 0.933 and 0.944. For the keyword ‘pneumonia’, the highest correlation was found 10, 8 and 10 days earlier in Google Trends, Baidu Index and Sina Weibo Index, with r = 0.893, 0.944 and 0.899, respectively. The lag correlation of new suspected cases was similar to the laboratory-confirmed cases, with a shorter lag time. The highest correlation was found 6, 8 and 8 days earlier for searches for the keyword ‘coronavirus’ in Google Trends, Baidu Index and Sina Weibo Index, with r = 0.912, 0.906 and 0.924, respectively. For the keyword ‘pneumonia’, the highest correlation was found all 8 days earlier in Google Trends, Baidu Index and Sina Weibo Index, with r = 0.960, 0.926 and 0.907, respectively.
title	Lag correlation between daily laboratory-confirmed/suspected cases and Internet searches
p	Figure 2 and the Table showed the lag Spearman correlations between the daily new laboratory-confirmed cases (upper panel) and suspected cases (lower panel) of COVID-19 and the Internet search data from Google Trends, Baidu Index and Sina Weibo Index. We found a high correlation with the Internet search data (r > 0.7) 8–10 days earlier for new laboratory-confirmed cases, and 5-7 days earlier for new suspected cases.
figure	Figure 2 Lag correlations between new laboratory-confirmed cases and suspected cases of COVID-19 and data from Google Trends, Baidu Index and Weibo Index for the keywords ‘coronavirus’ and ‘pneumonia’, China, January–February 2020
label	Figure 2
caption	Lag correlations between new laboratory-confirmed cases and suspected cases of COVID-19 and data from Google Trends, Baidu Index and Weibo Index for the keywords ‘coronavirus’ and ‘pneumonia’, China, January–February 2020
p	Lag correlations between new laboratory-confirmed cases and suspected cases of COVID-19 and data from Google Trends, Baidu Index and Weibo Index for the keywords ‘coronavirus’ and ‘pneumonia’, China, January–February 2020
table-wrap	Table Lag correlation coefficients and p values between Internet search data and daily new laboratory-confirmed/suspected COVID-19 cases, China, January–February 2020 Days earlier Google Trends Baidu Index Sina Weibo Index Coronavirus p Pneumonia p Coronavirus p Pneumonia p Coronavirus p Pneumonia p New laboratory-confirmed cases 0 0.176 0.370 −0.035 0.861 0.021 0.917 0.129 0.513 0.106 0.593 0.109 0.582 1 0.324 0.093 0.122 0.537 0.160 0.416 0.265 0.172 0.202 0.303 0.190 0.332 2 0.455 0.015 0.271 0.164 0.299 0.122 0.411 0.030 0.346 0.072 0.298 0.123 3 0.561 0.002 0.388 0.041 0.406 0.032 0.516 0.005 0.431 0.022 0.408 0.031 4 0.672 < 0.001 0.505 0.006 0.529 0.004 0.641 < 0.001 0.498 0.007 0.470 0.012 5 0.779 < 0.001 0.606 0.001 0.624 < 0.001 0.722 < 0.001 0.562 0.002 0.553 0.002 6 0.850 < 0.001 0.712 < 0.001 0.706 < 0.001 0.808 < 0.001 0.679 < 0.001 0.668 < 0.001 7 0.902 < 0.001 0.777 < 0.001 0.750 < 0.001 0.861 < 0.001 0.751 < 0.001 0.754 < 0.001 8 0.944 < 0.001 0.835 < 0.001 0.823 < 0.001 0.902 < 0.001 0.829 < 0.001 0.817 < 0.001 9 0.958 < 0.001 0.878 < 0.001 0.887 < 0.001 0.892 < 0.001 0.876 < 0.001 0.872 < 0.001 10 0.953 < 0.001 0.893 < 0.001 0.928 < 0.001 0.873 < 0.001 0.921 < 0.001 0.899 < 0.001 11 0.924 < 0.001 0.845 < 0.001 0.925 < 0.001 0.786 < 0.001 0.917 < 0.001 0.875 < 0.001 12 0.857 < 0.001 0.818 < 0.001 0.933 < 0.001 0.715 < 0.001 0.944 < 0.001 0.875 < 0.001 13 0.815 < 0.001 0.762 < 0.001 0.908 < 0.001 0.609 0.001 0.916 < 0.001 0.812 < 0.001 14 0.783 < 0.001 0.697 < 0.001 0.858 < 0.001 0.496 0.007 0.885 < 0.001 0.733 < 0.001 New suspected cases 0 −0.003 0.989 −0.372 0.073 −0.309 0.142 −0.091 0.671 −0.279 0.187 −0.309 0.142 1 0.246 0.246 −0.116 0.590 −0.068 0.753 0.141 0.511 −0.078 0.716 −0.103 0.630 2 0.413 0.045 0.104 0.630 0.125 0.560 0.346 0.098 0.089 0.680 0.050 0.818 3 0.614 0.001 0.312 0.138 0.352 0.091 0.551 0.005 0.253 0.233 0.248 0.243 4 0.768 < 0.001 0.514 0.010 0.538 0.007 0.697 < 0.001 0.431 0.035 0.383 0.065 5 0.832 < 0.001 0.687 < 0.001 0.670 < 0.001 0.816 < 0.001 0.520 0.009 0.501 0.013 6 0.912 < 0.001 0.771 < 0.001 0.725 < 0.001 0.895 < 0.001 0.672 < 0.001 0.670 < 0.001 7 0.933 < 0.001 0.850 < 0.001 0.830 < 0.001 0.914 < 0.001 0.813 < 0.001 0.872 < 0.001 8 0.875 < 0.001 0.960 < 0.001 0.906 < 0.001 0.926 < 0.001 0.924 < 0.001 0.907 < 0.001 9 0.787 < 0.001 0.865 < 0.001 0.882 < 0.001 0.850 < 0.001 0.883 < 0.001 0.899 < 0.001 10 0.744 < 0.001 0.827 < 0.001 0.841 < 0.001 0.766 < 0.001 0.818 < 0.001 0.832 < 0.001 11 0.671 < 0.001 0.770 < 0.001 0.790 < 0.001 0.698 < 0.001 0.781 < 0.001 0.802 < 0.001 12 0.544 0.006 0.693 < 0.001 0.686 < 0.001 0.559 0.005 0.697 < 0.001 0.683 < 0.001 13 0.482 0.017 0.578 0.003 0.583 0.003 0.454 0.026 0.622 0.001 0.600 0.002 14 0.497 0.013 0.448 0.028 0.547 0.006 0.288 0.173 0.609 0.002 0.550 0.005 Shaded text: high correlation with r > 0.7. Text in italics: highest correlation.
label	Table
caption	Lag correlation coefficients and p values between Internet search data and daily new laboratory-confirmed/suspected COVID-19 cases, China, January–February 2020
title	Lag correlation coefficients and p values between Internet search data and daily new laboratory-confirmed/suspected COVID-19 cases, China, January–February 2020
table	Days earlier Google Trends Baidu Index Sina Weibo Index Coronavirus p Pneumonia p Coronavirus p Pneumonia p Coronavirus p Pneumonia p New laboratory-confirmed cases 0 0.176 0.370 −0.035 0.861 0.021 0.917 0.129 0.513 0.106 0.593 0.109 0.582 1 0.324 0.093 0.122 0.537 0.160 0.416 0.265 0.172 0.202 0.303 0.190 0.332 2 0.455 0.015 0.271 0.164 0.299 0.122 0.411 0.030 0.346 0.072 0.298 0.123 3 0.561 0.002 0.388 0.041 0.406 0.032 0.516 0.005 0.431 0.022 0.408 0.031 4 0.672 < 0.001 0.505 0.006 0.529 0.004 0.641 < 0.001 0.498 0.007 0.470 0.012 5 0.779 < 0.001 0.606 0.001 0.624 < 0.001 0.722 < 0.001 0.562 0.002 0.553 0.002 6 0.850 < 0.001 0.712 < 0.001 0.706 < 0.001 0.808 < 0.001 0.679 < 0.001 0.668 < 0.001 7 0.902 < 0.001 0.777 < 0.001 0.750 < 0.001 0.861 < 0.001 0.751 < 0.001 0.754 < 0.001 8 0.944 < 0.001 0.835 < 0.001 0.823 < 0.001 0.902 < 0.001 0.829 < 0.001 0.817 < 0.001 9 0.958 < 0.001 0.878 < 0.001 0.887 < 0.001 0.892 < 0.001 0.876 < 0.001 0.872 < 0.001 10 0.953 < 0.001 0.893 < 0.001 0.928 < 0.001 0.873 < 0.001 0.921 < 0.001 0.899 < 0.001 11 0.924 < 0.001 0.845 < 0.001 0.925 < 0.001 0.786 < 0.001 0.917 < 0.001 0.875 < 0.001 12 0.857 < 0.001 0.818 < 0.001 0.933 < 0.001 0.715 < 0.001 0.944 < 0.001 0.875 < 0.001 13 0.815 < 0.001 0.762 < 0.001 0.908 < 0.001 0.609 0.001 0.916 < 0.001 0.812 < 0.001 14 0.783 < 0.001 0.697 < 0.001 0.858 < 0.001 0.496 0.007 0.885 < 0.001 0.733 < 0.001 New suspected cases 0 −0.003 0.989 −0.372 0.073 −0.309 0.142 −0.091 0.671 −0.279 0.187 −0.309 0.142 1 0.246 0.246 −0.116 0.590 −0.068 0.753 0.141 0.511 −0.078 0.716 −0.103 0.630 2 0.413 0.045 0.104 0.630 0.125 0.560 0.346 0.098 0.089 0.680 0.050 0.818 3 0.614 0.001 0.312 0.138 0.352 0.091 0.551 0.005 0.253 0.233 0.248 0.243 4 0.768 < 0.001 0.514 0.010 0.538 0.007 0.697 < 0.001 0.431 0.035 0.383 0.065 5 0.832 < 0.001 0.687 < 0.001 0.670 < 0.001 0.816 < 0.001 0.520 0.009 0.501 0.013 6 0.912 < 0.001 0.771 < 0.001 0.725 < 0.001 0.895 < 0.001 0.672 < 0.001 0.670 < 0.001 7 0.933 < 0.001 0.850 < 0.001 0.830 < 0.001 0.914 < 0.001 0.813 < 0.001 0.872 < 0.001 8 0.875 < 0.001 0.960 < 0.001 0.906 < 0.001 0.926 < 0.001 0.924 < 0.001 0.907 < 0.001 9 0.787 < 0.001 0.865 < 0.001 0.882 < 0.001 0.850 < 0.001 0.883 < 0.001 0.899 < 0.001 10 0.744 < 0.001 0.827 < 0.001 0.841 < 0.001 0.766 < 0.001 0.818 < 0.001 0.832 < 0.001 11 0.671 < 0.001 0.770 < 0.001 0.790 < 0.001 0.698 < 0.001 0.781 < 0.001 0.802 < 0.001 12 0.544 0.006 0.693 < 0.001 0.686 < 0.001 0.559 0.005 0.697 < 0.001 0.683 < 0.001 13 0.482 0.017 0.578 0.003 0.583 0.003 0.454 0.026 0.622 0.001 0.600 0.002 14 0.497 0.013 0.448 0.028 0.547 0.006 0.288 0.173 0.609 0.002 0.550 0.005
tr	Days earlier Google Trends Baidu Index Sina Weibo Index
th	Days earlier
th	Google Trends
th	Baidu Index
th	Sina Weibo Index
tr	Coronavirus p Pneumonia p Coronavirus p Pneumonia p Coronavirus p Pneumonia p
th	Coronavirus
th	p
th	Pneumonia
th	p
th	Coronavirus
th	p
th	Pneumonia
th	p
th	Coronavirus
th	p
th	Pneumonia
th	p
tr	New laboratory-confirmed cases 0 0.176 0.370 −0.035 0.861 0.021 0.917 0.129 0.513 0.106 0.593 0.109 0.582
td	New laboratory-confirmed cases
td	0
td	0.176
td	0.370
td	−0.035
td	0.861
td	0.021
td	0.917
td	0.129
td	0.513
td	0.106
td	0.593
td	0.109
td	0.582
tr	1 0.324 0.093 0.122 0.537 0.160 0.416 0.265 0.172 0.202 0.303 0.190 0.332
td	1
td	0.324
td	0.093
td	0.122
td	0.537
td	0.160
td	0.416
td	0.265
td	0.172
td	0.202
td	0.303
td	0.190
td	0.332
tr	2 0.455 0.015 0.271 0.164 0.299 0.122 0.411 0.030 0.346 0.072 0.298 0.123
td	2
td	0.455
td	0.015
td	0.271
td	0.164
td	0.299
td	0.122
td	0.411
td	0.030
td	0.346
td	0.072
td	0.298
td	0.123
tr	3 0.561 0.002 0.388 0.041 0.406 0.032 0.516 0.005 0.431 0.022 0.408 0.031
td	3
td	0.561
td	0.002
td	0.388
td	0.041
td	0.406
td	0.032
td	0.516
td	0.005
td	0.431
td	0.022
td	0.408
td	0.031
tr	4 0.672 < 0.001 0.505 0.006 0.529 0.004 0.641 < 0.001 0.498 0.007 0.470 0.012
td	4
td	0.672
td	< 0.001
td	0.505
td	0.006
td	0.529
td	0.004
td	0.641
td	< 0.001
td	0.498
td	0.007
td	0.470
td	0.012
tr	5 0.779 < 0.001 0.606 0.001 0.624 < 0.001 0.722 < 0.001 0.562 0.002 0.553 0.002
td	5
td	0.779
td	< 0.001
td	0.606
td	0.001
td	0.624
td	< 0.001
td	0.722
td	< 0.001
td	0.562
td	0.002
td	0.553
td	0.002
tr	6 0.850 < 0.001 0.712 < 0.001 0.706 < 0.001 0.808 < 0.001 0.679 < 0.001 0.668 < 0.001
td	6
td	0.850
td	< 0.001
td	0.712
td	< 0.001
td	0.706
td	< 0.001
td	0.808
td	< 0.001
td	0.679
td	< 0.001
td	0.668
td	< 0.001
tr	7 0.902 < 0.001 0.777 < 0.001 0.750 < 0.001 0.861 < 0.001 0.751 < 0.001 0.754 < 0.001
td	7
td	0.902
td	< 0.001
td	0.777
td	< 0.001
td	0.750
td	< 0.001
td	0.861
td	< 0.001
td	0.751
td	< 0.001
td	0.754
td	< 0.001
tr	8 0.944 < 0.001 0.835 < 0.001 0.823 < 0.001 0.902 < 0.001 0.829 < 0.001 0.817 < 0.001
td	8
td	0.944
td	< 0.001
td	0.835
td	< 0.001
td	0.823
td	< 0.001
td	0.902
td	< 0.001
td	0.829
td	< 0.001
td	0.817
td	< 0.001
tr	9 0.958 < 0.001 0.878 < 0.001 0.887 < 0.001 0.892 < 0.001 0.876 < 0.001 0.872 < 0.001
td	9
td	0.958
td	< 0.001
td	0.878
td	< 0.001
td	0.887
td	< 0.001
td	0.892
td	< 0.001
td	0.876
td	< 0.001
td	0.872
td	< 0.001
tr	10 0.953 < 0.001 0.893 < 0.001 0.928 < 0.001 0.873 < 0.001 0.921 < 0.001 0.899 < 0.001
td	10
td	0.953
td	< 0.001
td	0.893
td	< 0.001
td	0.928
td	< 0.001
td	0.873
td	< 0.001
td	0.921
td	< 0.001
td	0.899
td	< 0.001
tr	11 0.924 < 0.001 0.845 < 0.001 0.925 < 0.001 0.786 < 0.001 0.917 < 0.001 0.875 < 0.001
td	11
td	0.924
td	< 0.001
td	0.845
td	< 0.001
td	0.925
td	< 0.001
td	0.786
td	< 0.001
td	0.917
td	< 0.001
td	0.875
td	< 0.001
tr	12 0.857 < 0.001 0.818 < 0.001 0.933 < 0.001 0.715 < 0.001 0.944 < 0.001 0.875 < 0.001
td	12
td	0.857
td	< 0.001
td	0.818
td	< 0.001
td	0.933
td	< 0.001
td	0.715
td	< 0.001
td	0.944
td	< 0.001
td	0.875
td	< 0.001
tr	13 0.815 < 0.001 0.762 < 0.001 0.908 < 0.001 0.609 0.001 0.916 < 0.001 0.812 < 0.001
td	13
td	0.815
td	< 0.001
td	0.762
td	< 0.001
td	0.908
td	< 0.001
td	0.609
td	0.001
td	0.916
td	< 0.001
td	0.812
td	< 0.001
tr	14 0.783 < 0.001 0.697 < 0.001 0.858 < 0.001 0.496 0.007 0.885 < 0.001 0.733 < 0.001
td	14
td	0.783
td	< 0.001
td	0.697
td	< 0.001
td	0.858
td	< 0.001
td	0.496
td	0.007
td	0.885
td	< 0.001
td	0.733
td	< 0.001
tr	New suspected cases 0 −0.003 0.989 −0.372 0.073 −0.309 0.142 −0.091 0.671 −0.279 0.187 −0.309 0.142
td	New suspected cases
td	0
td	−0.003
td	0.989
td	−0.372
td	0.073
td	−0.309
td	0.142
td	−0.091
td	0.671
td	−0.279
td	0.187
td	−0.309
td	0.142
tr	1 0.246 0.246 −0.116 0.590 −0.068 0.753 0.141 0.511 −0.078 0.716 −0.103 0.630
td	1
td	0.246
td	0.246
td	−0.116
td	0.590
td	−0.068
td	0.753
td	0.141
td	0.511
td	−0.078
td	0.716
td	−0.103
td	0.630
tr	2 0.413 0.045 0.104 0.630 0.125 0.560 0.346 0.098 0.089 0.680 0.050 0.818
td	2
td	0.413
td	0.045
td	0.104
td	0.630
td	0.125
td	0.560
td	0.346
td	0.098
td	0.089
td	0.680
td	0.050
td	0.818
tr	3 0.614 0.001 0.312 0.138 0.352 0.091 0.551 0.005 0.253 0.233 0.248 0.243
td	3
td	0.614
td	0.001
td	0.312
td	0.138
td	0.352
td	0.091
td	0.551
td	0.005
td	0.253
td	0.233
td	0.248
td	0.243
tr	4 0.768 < 0.001 0.514 0.010 0.538 0.007 0.697 < 0.001 0.431 0.035 0.383 0.065
td	4
td	0.768
td	< 0.001
td	0.514
td	0.010
td	0.538
td	0.007
td	0.697
td	< 0.001
td	0.431
td	0.035
td	0.383
td	0.065
tr	5 0.832 < 0.001 0.687 < 0.001 0.670 < 0.001 0.816 < 0.001 0.520 0.009 0.501 0.013
td	5
td	0.832
td	< 0.001
td	0.687
td	< 0.001
td	0.670
td	< 0.001
td	0.816
td	< 0.001
td	0.520
td	0.009
td	0.501
td	0.013
tr	6 0.912 < 0.001 0.771 < 0.001 0.725 < 0.001 0.895 < 0.001 0.672 < 0.001 0.670 < 0.001
td	6
td	0.912
td	< 0.001
td	0.771
td	< 0.001
td	0.725
td	< 0.001
td	0.895
td	< 0.001
td	0.672
td	< 0.001
td	0.670
td	< 0.001
tr	7 0.933 < 0.001 0.850 < 0.001 0.830 < 0.001 0.914 < 0.001 0.813 < 0.001 0.872 < 0.001
td	7
td	0.933
td	< 0.001
td	0.850
td	< 0.001
td	0.830
td	< 0.001
td	0.914
td	< 0.001
td	0.813
td	< 0.001
td	0.872
td	< 0.001
tr	8 0.875 < 0.001 0.960 < 0.001 0.906 < 0.001 0.926 < 0.001 0.924 < 0.001 0.907 < 0.001
td	8
td	0.875
td	< 0.001
td	0.960
td	< 0.001
td	0.906
td	< 0.001
td	0.926
td	< 0.001
td	0.924
td	< 0.001
td	0.907
td	< 0.001
tr	9 0.787 < 0.001 0.865 < 0.001 0.882 < 0.001 0.850 < 0.001 0.883 < 0.001 0.899 < 0.001
td	9
td	0.787
td	< 0.001
td	0.865
td	< 0.001
td	0.882
td	< 0.001
td	0.850
td	< 0.001
td	0.883
td	< 0.001
td	0.899
td	< 0.001
tr	10 0.744 < 0.001 0.827 < 0.001 0.841 < 0.001 0.766 < 0.001 0.818 < 0.001 0.832 < 0.001
td	10
td	0.744
td	< 0.001
td	0.827
td	< 0.001
td	0.841
td	< 0.001
td	0.766
td	< 0.001
td	0.818
td	< 0.001
td	0.832
td	< 0.001
tr	11 0.671 < 0.001 0.770 < 0.001 0.790 < 0.001 0.698 < 0.001 0.781 < 0.001 0.802 < 0.001
td	11
td	0.671
td	< 0.001
td	0.770
td	< 0.001
td	0.790
td	< 0.001
td	0.698
td	< 0.001
td	0.781
td	< 0.001
td	0.802
td	< 0.001
tr	12 0.544 0.006 0.693 < 0.001 0.686 < 0.001 0.559 0.005 0.697 < 0.001 0.683 < 0.001
td	12
td	0.544
td	0.006
td	0.693
td	< 0.001
td	0.686
td	< 0.001
td	0.559
td	0.005
td	0.697
td	< 0.001
td	0.683
td	< 0.001
tr	13 0.482 0.017 0.578 0.003 0.583 0.003 0.454 0.026 0.622 0.001 0.600 0.002
td	13
td	0.482
td	0.017
td	0.578
td	0.003
td	0.583
td	0.003
td	0.454
td	0.026
td	0.622
td	0.001
td	0.600
td	0.002
tr	14 0.497 0.013 0.448 0.028 0.547 0.006 0.288 0.173 0.609 0.002 0.550 0.005
td	14
td	0.497
td	0.013
td	0.448
td	0.028
td	0.547
td	0.006
td	0.288
td	0.173
td	0.609
td	0.002
td	0.550
td	0.005
table-wrap-foot	Shaded text: high correlation with r > 0.7. Text in italics: highest correlation.
p	Shaded text: high correlation with r > 0.7. Text in italics: highest correlation.
p	For new laboratory-confirmed cases, the highest correlation was found 9, 12 and 12 days earlier for searches for the keyword ‘coronavirus’ in Google Trends, Baidu Index and Sina Weibo Index with, respectively, r = 0.958, 0.933 and 0.944. For the keyword ‘pneumonia’, the highest correlation was found 10, 8 and 10 days earlier in Google Trends, Baidu Index and Sina Weibo Index, with r = 0.893, 0.944 and 0.899, respectively.
p	The lag correlation of new suspected cases was similar to the laboratory-confirmed cases, with a shorter lag time. The highest correlation was found 6, 8 and 8 days earlier for searches for the keyword ‘coronavirus’ in Google Trends, Baidu Index and Sina Weibo Index, with r = 0.912, 0.906 and 0.924, respectively. For the keyword ‘pneumonia’, the highest correlation was found all 8 days earlier in Google Trends, Baidu Index and Sina Weibo Index, with r = 0.960, 0.926 and 0.907, respectively.
sec	Discussion Our study demonstrated that the data obtained from Google Trends, Baidu Index and Sina Weibo Index on searches for the keywords ‘coronavirus’ and ‘pneumonia’ correlated with the published NHC data on daily incidence of laboratory-confirmed and suspected cases of COVID-19, with the maximum r > 0.89. We also found that the peak interest for these keywords in Internet search engines and social media data was 10–14 days earlier than the incidence peak of COVID-19 published by the NHC. The lag correlation showed a maximum correlation at 8–12 days for laboratory-confirmed cases and 6–8 days for suspected cases. COVID-19 is a rapidly spreading infectious disease with, at the time of submission, more than 80,000 cases and a mortality so far known to be 3.4% [10]. It is important to predict the development of this outbreak as early and as reliably as possible, in order to take action to prevent its spread. Our data showed that the two popularly used Internet search engines, Google and Baidu, and the social media platform, Sina Weibo, were able to predict the disease outbreak 1–2 weeks earlier than the traditional surveillance systems. The role of Internet surveillance tools in early prediction of other epidemics has been reported previously, including for influenza [4], dengue fever [5], H1N1 [6], Zika [7], measles [8] and Middle East respiratory syndrome [9]. The availability of early information about infectious diseases through Internet search engines and social media will be helpful for making decisions related to disease control and prevention. Internet search data have been shown to enable the monitoring of Middle East respiratory syndrome 3 days before laboratory confirmations [9]. However, our results showed a much longer lag time for reported new laboratory-confirmed and suspected COVID-19 cases compared with digital surveillance data. There are several explanations. Firstly, COVID-19 is a novel disease just recently recognised. The first version of a guideline for diagnosis and management of COVID-19 was announced on 16 January 2020. It took time for the medical professionals to learn about the virus and the disease in order to make correct diagnosis. Secondly, the diagnosis of COVID-19 requires two independent confirmatory laboratory tests, which should be taken at least 1 day apart. Our results showed that the lag correlation is shorter for the suspected than for laboratory-confirmed cases. Thirdly, the supply of laboratory testing kits may have been insufficient in the early stages of the coronavirus outbreak, which would have limited the number of patients that can be confirmed. Finally, the Internet searches and social media mentions are not only initiated by the patients and their family members, but also globally by the general public who are concerned about this rapidly spreading disease. In addition, we found that the data from the Baidu Index and Sina Weibo Index could monitor the number of daily new confirmed and suspected cases from the NHC earlier than the data from Google Trends. A possible explanation is that the Google is not a major search engine used in China, where Baidu and Sina Weibo are widely used. The peak in the Sina Weibo Index was reached earlier than in Google Trends and Baidu Index. This suggests that Sina Weibo, which also serves as a social medium, disseminated the information faster than traditional websites. COVID-19 was firstly reported as ‘pneumonia of unknown aetiology’ or ‘pneumonia of unknown cause’ in late December 2019. On 8 January 2020, a novel coronavirus was identified as the cause of this disease. The disease was first named Novel coronavirus pneumonia by the NHC of China on 8 February and later ‘coronavirus disease 2019’ (abbreviated ‘COVID-19’) on 11 February by the WHO. Our search period was defined from January 16 to February 11. Therefore, we think that the two keywords ‘pneumonia’ and ‘coronavirus’ were sufficient to include most Internet content related to COVID-19 in this period. We also used other terms such as ‘新冠‘ (novel coronavirus), ‘新型冠状病毒肺炎’ (novel coronavirus pneumonia) as keywords but they returned much smaller numbers of queries and posts and we did therefore not include them in the analysis. It is also notable that the strength of correlation was different for different keywords. On Google, the keyword ‘coronavirus’ had the highest correlation coefficient (r = 0.958) with daily new laboratory-confirmed cases, and ‘pneumonia’ had the highest correlation coefficient with daily new suspected cases (r = 0.960). We found the same pattern in the Baidu Index and Sina Weibo Index. An explanation could be that ‘coronavirus’ is linked to the viral pathogen which should be investigated by a laboratory test, while ‘pneumonia’ is a clinical term and should link stronger to the suspected cases that are based on clinical and imaging evidence. A limitation of our study is its retrospective nature. If the Internet search engines and social media data were used in a real-time surveillance system, finding the best lag time would be a challenge because we would not have any training data to calibrate the analysis for a new disease.
title	Discussion
p	Our study demonstrated that the data obtained from Google Trends, Baidu Index and Sina Weibo Index on searches for the keywords ‘coronavirus’ and ‘pneumonia’ correlated with the published NHC data on daily incidence of laboratory-confirmed and suspected cases of COVID-19, with the maximum r > 0.89. We also found that the peak interest for these keywords in Internet search engines and social media data was 10–14 days earlier than the incidence peak of COVID-19 published by the NHC. The lag correlation showed a maximum correlation at 8–12 days for laboratory-confirmed cases and 6–8 days for suspected cases.
p	COVID-19 is a rapidly spreading infectious disease with, at the time of submission, more than 80,000 cases and a mortality so far known to be 3.4% [10]. It is important to predict the development of this outbreak as early and as reliably as possible, in order to take action to prevent its spread. Our data showed that the two popularly used Internet search engines, Google and Baidu, and the social media platform, Sina Weibo, were able to predict the disease outbreak 1–2 weeks earlier than the traditional surveillance systems. The role of Internet surveillance tools in early prediction of other epidemics has been reported previously, including for influenza [4], dengue fever [5], H1N1 [6], Zika [7], measles [8] and Middle East respiratory syndrome [9]. The availability of early information about infectious diseases through Internet search engines and social media will be helpful for making decisions related to disease control and prevention.
p	Internet search data have been shown to enable the monitoring of Middle East respiratory syndrome 3 days before laboratory confirmations [9]. However, our results showed a much longer lag time for reported new laboratory-confirmed and suspected COVID-19 cases compared with digital surveillance data. There are several explanations. Firstly, COVID-19 is a novel disease just recently recognised. The first version of a guideline for diagnosis and management of COVID-19 was announced on 16 January 2020. It took time for the medical professionals to learn about the virus and the disease in order to make correct diagnosis. Secondly, the diagnosis of COVID-19 requires two independent confirmatory laboratory tests, which should be taken at least 1 day apart. Our results showed that the lag correlation is shorter for the suspected than for laboratory-confirmed cases. Thirdly, the supply of laboratory testing kits may have been insufficient in the early stages of the coronavirus outbreak, which would have limited the number of patients that can be confirmed. Finally, the Internet searches and social media mentions are not only initiated by the patients and their family members, but also globally by the general public who are concerned about this rapidly spreading disease.
p	In addition, we found that the data from the Baidu Index and Sina Weibo Index could monitor the number of daily new confirmed and suspected cases from the NHC earlier than the data from Google Trends. A possible explanation is that the Google is not a major search engine used in China, where Baidu and Sina Weibo are widely used. The peak in the Sina Weibo Index was reached earlier than in Google Trends and Baidu Index. This suggests that Sina Weibo, which also serves as a social medium, disseminated the information faster than traditional websites.
p	COVID-19 was firstly reported as ‘pneumonia of unknown aetiology’ or ‘pneumonia of unknown cause’ in late December 2019. On 8 January 2020, a novel coronavirus was identified as the cause of this disease. The disease was first named Novel coronavirus pneumonia by the NHC of China on 8 February and later ‘coronavirus disease 2019’ (abbreviated ‘COVID-19’) on 11 February by the WHO. Our search period was defined from January 16 to February 11. Therefore, we think that the two keywords ‘pneumonia’ and ‘coronavirus’ were sufficient to include most Internet content related to COVID-19 in this period. We also used other terms such as ‘新冠‘ (novel coronavirus), ‘新型冠状病毒肺炎’ (novel coronavirus pneumonia) as keywords but they returned much smaller numbers of queries and posts and we did therefore not include them in the analysis.
p	It is also notable that the strength of correlation was different for different keywords. On Google, the keyword ‘coronavirus’ had the highest correlation coefficient (r = 0.958) with daily new laboratory-confirmed cases, and ‘pneumonia’ had the highest correlation coefficient with daily new suspected cases (r = 0.960). We found the same pattern in the Baidu Index and Sina Weibo Index. An explanation could be that ‘coronavirus’ is linked to the viral pathogen which should be investigated by a laboratory test, while ‘pneumonia’ is a clinical term and should link stronger to the suspected cases that are based on clinical and imaging evidence.
p	A limitation of our study is its retrospective nature. If the Internet search engines and social media data were used in a real-time surveillance system, finding the best lag time would be a challenge because we would not have any training data to calibrate the analysis for a new disease.
sec	Conclusion This study reveals the advantages of Internet surveillance using Sina Weibo Index, Google Trends and Baidu Index to monitor a new infectious disease. Reliable data can be obtained early at low cost. The Internet surveillance data provided an accurate and timely prediction about the outbreak and progression of COVID-19.
title	Conclusion
p	This study reveals the advantages of Internet surveillance using Sina Weibo Index, Google Trends and Baidu Index to monitor a new infectious disease. Reliable data can be obtained early at low cost. The Internet surveillance data provided an accurate and timely prediction about the outbreak and progression of COVID-19.
back	Acknowledgements Funding statement: This study was supported by the Grant for Key Disciplinary Project of Clinical Medicine under the Guangdong High-level University Development Program (002-18119101). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Conflict of interest: None declared. Authors’ contributions: CL and LJC collected the data, analysed the data and drafted the manuscript. XC, LJC, CPP and HC revised the manuscript. MZ and HC convened the idea and designed the study.
ack	Acknowledgements Funding statement: This study was supported by the Grant for Key Disciplinary Project of Clinical Medicine under the Guangdong High-level University Development Program (002-18119101). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
title	Acknowledgements
p	Funding statement: This study was supported by the Grant for Key Disciplinary Project of Clinical Medicine under the Guangdong High-level University Development Program (002-18119101). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
notes	Conflict of interest: None declared. Authors’ contributions: CL and LJC collected the data, analysed the data and drafted the manuscript. XC, LJC, CPP and HC revised the manuscript. MZ and HC convened the idea and designed the study.
footnote	Conflict of interest: None declared.
p	Conflict of interest: None declared.
footnote	Authors’ contributions: CL and LJC collected the data, analysed the data and drafted the manuscript. XC, LJC, CPP and HC revised the manuscript. MZ and HC convened the idea and designed the study.
p	Authors’ contributions: CL and LJC collected the data, analysed the data and drafted the manuscript. XC, LJC, CPP and HC revised the manuscript. MZ and HC convened the idea and designed the study.

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